Data-Driven Representation Model of Urban Movement Space

Abstract

Tracking urban mobility with current heterogeneous sensing capabilities has opened a wide research area on analytical and predictive data-driven models for improvements in transport operations and planning. These improvements are applicable for individual users, service providers and decision-makers. People, vehicles and goods move along the city according to the physical resources (roads, bike-lanes, side-walks...) and non-physical resources (such as scheduled public transportation services). We present this set of resources as the Urban Movement Space (UMS). We collect the main challenges and research foundations that geoinformatic approaches need to cope when tackling transportation resources and mobility data. The work presented in this paper proposes a conceptual modelling framework to represent the urban movement space, in order to match observed tracking data accordingly, and allow further analytical queries. Our approach combines an open free-space and network-based space to model the time-varying urban movement space, considering seasonality and uncertainty of multimodal travel options.